Instance segmentation aims to predict the semantic and instance labels of each image pixel. Compared to object detection [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and semantic segmentation [9]}, [10]}, [11]}, instance segmentation provides more fine-grained information but is more challenging and attracts more and more research interests of the community. Many methods [12]}, [13]}, [14]}, [15]} and datasets [16]}, [17]}, [18]}, [19]} continue to emerge in this field. However, most of them focus on regularly shaped objects and only a few [20]}, [18]} study irregular ones, which are thin, curved, or having complex boundary and can not be well-represented by regularly rectangular boxes. A more clear definition of irregular shape is “the area of the bounding box is much larger than the area of instance mask or the aspect ratio of the bounding box is large”. We think the insufficient exploration of this direction is caused by the lack of corresponding datasets.
